Consensus-based transport event severity

ABSTRACT

An example operation includes one or more of determining, by a server, an event associated with a transport, receiving, by the server, atypical data related to the transport from a plurality of devices over various times prior to the event, analyzing, by the server, the atypical data, forming, by the server, a consensus based on the analyzed atypical data to determine a severity of the event, and determining, by the server, an action to take based on the severity.

BACKGROUND

Vehicles or transports, such as cars, motorcycles, trucks, planes,trains, etc., generally provide transportation needs to occupants and/orgoods in a variety of ways. Functions related to transports may beidentified and utilized by various computing devices, such as asmartphone or a computer.

Vehicles and transports may sometimes be involved in various events thatmay include accidents and transport failures and breakdowns of varioustypes. While a transport may be able to provide significant data withrespect to the transport condition, it is often unable to provide usefuldata as to what led to or caused the event. Traditionally, obtainingsuch data is performed by accident or other investigators. Therefore,what is needed is a way to determine event causes and involvementwithout requiring the expense and time of various investigativepersonnel on-scene.

SUMMARY

One example embodiment provides a method that includes one or more ofreceiving data, by a transport, from a device in proximity to thetransport, determining, by the transport, a dangerous situation based onthe data, obtaining consensus, by the transport, to validate thedangerous situation, and notifying the device, by the transport, basedon the consensus. The notification includes the dangerous situation, andthe data includes one or more of a speed, a direction, and a distance ofthe device from the transport. The dangerous situation is validated byconsensus from one or more other devices proximate to the transport andone or more other transports proximate to the transport.

Another example embodiment provides a transport that includes aprocessor and a memory, coupled to the processor. The memory includesinstructions that when executed by the processor are configured to oneor more of receive data, by the transport, from a device in proximity tothe transport, determine, by the transport, a dangerous situation basedon the data, obtain consensus, by the transport, and notify the device,by the transport, based on the consensus. The notification includes thedangerous situation, and the data includes one or more of a speed, adirection, and a distance of the device from the transport. Thedangerous situation is validated by consensus from one or more otherdevices proximate to the transport and one or more other transportsproximate to the transport.

A further example embodiment provides a non-transitory computer readablemedium including instructions, that when read by a processor, cause theprocessor to perform one or more of receiving data, by a transport, froma device in proximity to the transport, determining, by the transport, adangerous situation based on the data, obtaining consensus, by thetransport, to validate the dangerous situation, and notifying thedevice, by the transport, based on the consensus. The notificationincludes the dangerous situation, and the data includes one or more of aspeed, a direction, and a distance of the device from the transport. Thedangerous situation is validated by, consensus from one or more otherdevices proximate to the transport and one or more other transportsproximate to the transport.

One example embodiment provides a method that includes one or more ofreceiving, by a server, primary data related to an event from one ormore devices associated with one or more transports, receiving, by theserver, secondary data from one or more other devices proximate to theevent, analyzing, by the server, the primary data and the secondary datato determine a severity of the event, and sending, from the server, afirst notification to the one or more devices associated with theprimary data and a second notification to one or more of the otherdevices associated with the secondary data, based on the severity of theevent.

Another example embodiment provides a transport that includes aprocessor and a memory, coupled to the processor. The memory includesinstructions that when executed by the processor are configured to oneor more of receive, by a server, primary data related to an event fromone or more devices associated with one or more transports, receive, bythe server, secondary data from one or more other devices proximate tothe event, analyze, by the server, the primary data and the secondarydata to determine a severity of the event, and send, from the server, afirst notification to the one or more devices associated with theprimary data and a second notification to one or more of the otherdevices associated with the secondary data, based on the severity of theevent.

A further example embodiment provides a non-transitory computer readablemedium including instructions, that when read by a processor, cause theprocessor to perform one or more of receiving, by a server, primary datarelated to an event from one or more devices associated with one or moretransports, receiving, by the server, secondary data from one or moreother devices proximate to the event, analyzing, by the server, theprimary data and the secondary data to determine a severity of theevent, and sending, from the server, a first notification to the one ormore devices associated with the primary data and a second notificationto one or more of the other devices associated with the secondary data,based on the severity of the event.

One example embodiment provides a method that includes one or more ofreceiving, by a server, first data from a transport experiencing a nearaccident with a device external to the transport, determining, by theserver, that the location of the transport is a dangerous location andthe maneuvering of the transport constitutes the near accident based onthe first data, and sending a first notification, by the server, toanother transport proximate to the dangerous location. The firstnotification includes information related to the dangerous location andthe near accident. The first data includes a location of the transportand information related to a maneuvering of the transport.

Another example embodiment provides a transport that includes aprocessor and a memory, coupled to the processor. The memory includesinstructions that when executed by the processor are configured to oneor more of receive, by a server, first data from the transportexperiencing a near accident with a device external to the transport,determine, by the server, that the location of the transport is adangerous location and the transport maneuver constitutes the nearaccident based on the first data, and send a first notification, by theserver, to another transport proximate to the dangerous location. Thefirst notification includes information related to the dangerouslocation and the near accident, and the first data includes a locationof the transport and information related to a transport maneuver.

A further example embodiment provides a non-transitory computer readablemedium including instructions, that when read by a processor, cause theprocessor to perform one or more of receiving, by a server, first datafrom a transport experiencing a near accident with a device external tothe transport, determining, by the server, that the location of thetransport is a dangerous location and the maneuvering of the transportconstitutes the near accident based on the first data, and sending afirst notification, by the server, to another transport proximate to thedangerous location. The first notification includes information relatedto the dangerous location and the near accident, and the first dataincludes a location of the transport and information related to amaneuvering of the transport.

One example embodiment provides a method that includes one or more ofdetermining, by a transport, a number of items in the transport,determining, by the transport, a position of each of the items, and inresponse to a dangerous situation associated with the transportoccurring, providing, by the transport, a safety notification associatedwith at least one of the items.

Another example embodiment provides a transport that includes aprocessor and a memory, coupled to the processor. The memory includesinstructions that when executed by the processor are configured to oneor more of determine, by a transport, a number of items in thetransport, determine, by the transport, a position of each of the items,and in response to a dangerous situation associated with the transportoccurring, provide, by the transport, a safety notification associatedwith at least one of the items.

A further example embodiment provides a non-transitory computer readablemedium including instructions, that when read by a processor, cause theprocessor to perform one or more of determining, by a transport, anumber of items in the transport, determining, by the transport, aposition of each of the items, and in response to a dangerous situationassociated with the transport occurring, providing, by the transport, asafety notification associated with at least one of the items.

One example embodiment provides a method that includes one or more ofdetermining, by a server, an event associated with a transport,receiving, by the server, atypical data related to the transport from aplurality of devices over various times prior to the event, analyzing,by the server, the atypical data, forming, by the server, a consensusbased on the analyzed atypical data to determine a severity of theevent, and determining, by the server, an action to take based on theseverity.

Another example embodiment provides a transport that includes aprocessor and a memory, coupled to the processor. The memory includesinstructions that when executed by the processor are configured to oneor more of determine, by a server, an event associated with a transport,receive, by the server, atypical data related to the transport from aplurality of devices over various times prior to the event, analyze, bythe server, the atypical data, form, by the server, a consensus based onthe analyzed atypical data to determine a severity of the event, anddetermine, by server, an action to take based on the severity.

A further example embodiment provides a non-transitory computer readablemedium including instructions, that when read by a processor, cause theprocessor to perform one or more of determining, by a server, an eventassociated with a transport, receiving, by the server, atypical datarelated to the transport from a plurality of devices over various timesprior to the event, analyzing, by the server, the atypical data,forming, by the server, a consensus based on the analyzed atypical datato determine a severity of the event, and determining, by the server, anaction to take based on the severity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates an example diagram of transport system for resolvingdangerous situations, according to example embodiments.

FIG. 1B illustrates an example diagram of a transport system forprocessing primary and secondary data, according to example embodiments.

FIG. 1C illustrates an example diagram of a transport system forresolving and improving near accident conditions, according to exampleembodiments.

FIG. 1D illustrates an example diagram of a transport system forresolving item dispositions in response to a dangerous situation,according to example embodiments.

FIG. 1E illustrates an example diagram of a transport system for takingactions based on received atypical data, according to exampleembodiments.

FIG. 2A illustrates a transport network diagram, according to exampleembodiments.

FIG. 2B illustrates another transport network diagram, according toexample embodiments.

FIG. 2C illustrates yet another transport network diagram, according toexample embodiments.

FIG. 2D illustrates a further transport network diagram, according toexample embodiments.

FIG. 2E illustrates a yet further transport network diagram, accordingto example embodiments.

FIG. 2F illustrates a yet further transport network diagram, accordingto example embodiments.

FIG. 2G illustrates a yet further transport network diagram, accordingto example embodiments.

FIG. 3A illustrates a flow diagram, according to example embodiments.

FIG. 3B illustrates another flow diagram, according to exampleembodiments.

FIG. 3C illustrates yet another flow diagram, according to exampleembodiments.

FIG. 3D illustrates yet another flow diagram, according to exampleembodiments.

FIG. 3E illustrates yet another flow diagram, according to exampleembodiments.

FIG. 4 illustrates a machine learning transport network diagram,according to example embodiments.

FIG. 5A illustrates an example vehicle configuration for managingdatabase transactions associated with a vehicle, according to exampleembodiments.

FIG. 5B illustrates another example vehicle configuration for managingdatabase transactions conducted among various vehicles, according toexample embodiments.

FIG. 6A illustrates a blockchain architecture configuration, accordingto example embodiments.

FIG. 6B illustrates another blockchain configuration, according toexample embodiments.

FIG. 6C illustrates a blockchain configuration for storing blockchaintransaction data, according to example embodiments.

FIG. 6D illustrates example data blocks, according to exampleembodiments.

FIG. 7 illustrates an example system that supports one or more of theexample embodiments.

DETAILED DESCRIPTION

It will be readily understood that the instant components, as generallydescribed and illustrated in the figures herein, may be arranged anddesigned in a wide variety of different configurations. Thus, thefollowing detailed description of the embodiments of at least one of amethod, apparatus, non-transitory computer readable medium and system,as represented in the attached figures, is not intended to limit thescope of the application as claimed but is merely representative ofselected embodiments.

The instant features, structures, or characteristics as describedthroughout this specification may be combined in any suitable manner inone or more embodiments. For example, the usage of the phrases “exampleembodiments”, “some embodiments”, or other similar language, throughoutleast this specification refers to the fact that a particular feature,structure, or characteristic described in connection with the embodimentmay be included in at one embodiment. Thus, appearances of the phrases“example embodiments”, “in some embodiments”, “in other embodiments”, orother similar language, throughout this specification do not necessarilyall refer to the same group of embodiments, and the described features,structures, or characteristics may be combined in any suitable manner inone or more embodiments. In the diagrams, any connection betweenelements can permit one-way and/or two-way communication even if thedepicted connection is a one-way or two-way arrow. In the currentapplication, a transport may include one or more of cars, trucks,motorcycles, scooters, bicycles, boats, recreational vehicles, planes,and any object that may be used to transport people and or goods fromone location to another.

In addition, while the term “message” may have been used in thedescription of embodiments, the application may be applied to many typesof network data, such as, a packet, frame, datagram, etc. The term“message” also includes packet, frame, datagram, and any equivalentsthereof. Furthermore, while certain types of messages and signaling maybe depicted in exemplary embodiments they are not limited to a certaintype of message, and the application is not limited to a certain type ofsignaling.

Example embodiments provide methods, systems, components, non-transitorycomputer readable media, devices, and/or networks, which provide atleast one of: a transport (also referred to as a vehicle herein) a datacollection system, a data monitoring system, a verification system, anauthorization system and a vehicle data distribution system. The vehiclestatus condition data, received in the form of communication updatemessages, such as wireless data network communications and/or wiredcommunication messages, may be received and processed to identifyvehicle/transport status conditions and provide feedback as to thecondition changes of a transport. In one example, a user profile may beapplied to a particular transport/vehicle to authorize a current vehicleevent, service stops at service stations, and to authorize subsequentvehicle rental services.

Within the communication infrastructure, a decentralized database is adistributed storage system, which includes multiple nodes thatcommunicate with each other. A blockchain is an example of adecentralized database, which includes an append-only immutable datastructure (i.e. a distributed ledger) capable of maintaining recordsbetween untrusted parties. The untrusted parties are referred to hereinas peers, nodes or peer nodes. Each peer maintains a copy of thedatabase records and no single peer can modify the database recordswithout a consensus being reached among the distributed peers. Forexample, the peers may execute a consensus protocol to validateblockchain storage entries, group the storage entries into blocks, andbuild a hash chain via the blocks. This process forms the ledger byordering the storage entries, as is necessary, for consistency. In apublic or permissionless blockchain, anyone can participate without aspecific identity. Public blockchains can involve cryptocurrencies anduse consensus based on various protocols such as proof of work (PoW). Onthe other hand, a permissioned blockchain database provides a system,which can secure interactions among a group of entities which share acommon goal, but which do not or cannot fully trust one another, such asbusinesses that exchange funds, goods, information, and the like. Theinstant application can function in a permissioned and/or apermissionless blockchain setting.

Smart contracts are trusted distributed applications which leveragetamper-proof properties of the shared or distributed ledger (i.e., whichmay be in the form of a blockchain) database and an underlying agreementbetween member nodes which is referred to as an endorsement orendorsement policy. In general, blockchain entries are “endorsed” beforebeing committed to the blockchain while entries, which are not endorsedare disregarded. A typical endorsement policy allows smart contractexecutable code to specify endorsers for an entry in the form of a setof peer nodes that are necessary for endorsement. When a client sendsthe entry to the peers specified in the endorsement policy, the entry isexecuted to validate the entry. After validation, the entries enter anordering phase in which a consensus protocol is used to produce anordered sequence of endorsed entries grouped into blocks.

Nodes are the communication entities of the blockchain system. A “node”may perform a logical function in the sense that multiple nodes ofdifferent types can run on the same physical server. Nodes are groupedin trust domains and are associated with logical entities that controlthem in various ways. Nodes may include different types, such as aclient or submitting-client node which submits an entry-invocation to anendorser (e.g., peer), and broadcasts entry-proposals to an orderingservice (e.g., ordering node). Another type of node is a peer node whichcan receive client submitted entries, commit the entries and maintain astate and a copy of the ledger of blockchain entries. Peers can alsohave the role of an endorser, although it is not a requirement. Anordering-service-node or orderer is a node running the communicationservice for all nodes, and which implements a delivery guarantee, suchas a broadcast to each of the peer nodes in the system when committingentries and modifying a world state of the blockchain, which is anothername for the initial blockchain entry, which normally includes controland setup information.

A ledger is a sequenced, tamper-resistant record of all statetransitions of a blockchain. State transitions may result from smartcontract executable code invocations (i.e., entries) submitted byparticipating parties (e.g., client nodes, ordering nodes, endorsernodes, peer nodes, etc.). An entry may result in a set of assetkey-value pairs being committed to the ledger as one or more operands,such as creates, updates, deletes, and the like. The ledger includes ablockchain (also referred to as a chain), which is used to store animmutable, sequenced record in blocks. The ledger also includes a statedatabase, which maintains a current state of the blockchain. There istypically one ledger per channel. Each peer node maintains a copy of theledger for each channel of which they are a member.

A chain is an entry log, which is structured as hash-linked blocks, andeach block contains a sequence of N entries where N is equal to orgreater than one. The block header includes a hash of the block'sentries, as well as a hash of the prior block's header. In this way, allentries on the ledger may be sequenced and cryptographically linkedtogether. Accordingly, it is not possible to tamper with the ledger datawithout breaking the hash links. A hash of a most recently addedblockchain block represents every entry on the chain that has comebefore it, making it possible to ensure that all peer nodes are in aconsistent and trusted state. The chain may be stored on a peer nodefile system (i.e., local, attached storage, cloud, etc.), efficientlysupporting the append-only nature of the blockchain workload.

The current state of the immutable ledger represents the latest valuesfor all keys that are included in the chain entry log. Because thecurrent state represents the latest key values known to a channel, it issometimes referred to as a world state. Smart contract executable codeinvocations execute entries against the current state data of theledger. To make these smart contract executable code interactionsefficient, the latest values of the keys may be stored in a statedatabase. The state database may be simply an indexed view into thechain's entry log, it can therefore be regenerated from the chain at anytime. The state database may automatically be recovered (or generated ifneeded) upon peer node startup, and before entries are accepted.

A blockchain is different from a traditional database in that theblockchain is not a central storage but rather a decentralized,immutable, and secure storage, where nodes must share in changes torecords in the storage. Some properties that are inherent in blockchainand which help implement the blockchain include, but are not limited to,an immutable ledger, smart contracts, security, privacy,decentralization, consensus, endorsement, accessibility, and the like.

Example embodiments provide a way for providing a vehicle service to aparticular vehicle and/or requesting user associated with a user profilethat is applied to the vehicle. For example, a user may be the owner ofa vehicle or the operator of a vehicle owned by another party. Thevehicle may require service at certain intervals and the service needsmay require authorization prior to permitting the services to bereceived. Also, service centers may offer services to vehicles in anearby area based on the vehicle's current route plan and a relativelevel of service requirements (e.g., immediate, severe, intermediate,minor, etc.). The vehicle needs may be monitored via one or moresensors, which report sensed data to a central controller computerdevice in the vehicle, which in turn, is forwarded to a managementserver for review and action.

A sensor may be located on one or more of the interior of the transport,the exterior of the transport, on a fixed object apart from thetransport, and on another transport near to the transport. The sensormay also be associated with the transport's speed, the transport'sbraking, the transport's acceleration, fuel levels, service needs, thegear-shifting of the transport, the transport's steering, and the like.The notion of a sensor may also be a device, such as a mobile device.Also, sensor information may be used to identify whether the vehicle isoperating safely and whether the occupant user has engaged in anyunexpected vehicle conditions, such as during the vehicle access period.Vehicle information collected before, during and/or after a vehicle'soperation may be identified and stored in a transaction on ashared/distributed ledger, which may be generated and committed to theimmutable ledger as determined by a permission granting consortium, andthus in a “decentralized” manner, such as via a blockchain membershipgroup.

Each interested party (i.e., company, agency, etc.) may want to limitthe exposure of private information, and therefore the blockchain andits immutability can limit the exposure and manage permissions for eachparticular user vehicle profile. A smart contract may be used to providecompensation, quantify a user profile score/rating/review, apply vehicleevent permissions, determine when service is needed, identify acollision and/or degradation event, identify a safety concern event,identify parties to the event and provide distribution to registeredentities seeking access to such vehicle event data. Also, the resultsmay be identified, and the necessary information can be shared among theregistered companies and/or individuals based on a “consensus” approachassociated with the blockchain. Such an approach could not beimplemented on a traditional centralized database.

Every autonomous driving system is built on a whole suite of softwareand an array of sensors. Machine learning, lidar projectors, radar, andultrasonic sensors all work together to create a living map of the worldthat a self-driving car can navigate. Most companies in the race to fullautonomy are relying on the same basic technological foundations oflidar+radar+cameras+ultrasonic, with a few notable exceptions.

In another embodiment, GPS, maps and other cameras and sensors are usedin autonomous vehicles without lidar as lidar is often viewed as beingexpensive and unnecessary. Researchers have determined that stereocameras are a low-cost alternative to the more expensive lidarfunctionality.

The instant application includes, in certain embodiments, authorizing avehicle for service via an automated and quick authentication scheme.For example, driving up to a charging station or fuel pump may beperformed by a vehicle operator, and the authorization to receive chargeor fuel may be performed without any delays provided the authorizationis received by the service station. A vehicle may provide acommunication signal that provides an identification of a vehicle thathas a currently active profile linked to an account that is authorizedto accept a service which can be later rectified by compensation.Additional measures may be used to provide further authentication, suchas another identifier may be sent from the user's device wirelessly tothe service center to replace or supplement the first authorizationeffort between the transport and the service center with an additionalauthorization effort.

Data shared and received may be stored in a database, which maintainsdata in one single database (e.g., database server) and generally at oneparticular location. This location is often a central computer, forexample, a desktop central processing unit (CPU), a server CPU, or amainframe computer. Information stored on a centralized database istypically accessible from multiple different points. A centralizeddatabase is easy to manage, maintain, and control, especially forpurposes of security because of its single location. Within acentralized database, data redundancy is minimized as a single storingplace of all data also implies that a given set of data only has oneprimary record.

FIG. 1A illustrates an example diagram of a transport system forresolving dangerous situations 100. A dangerous situation as describedherein is a traffic situation involving a moving transport 104 and adevice 118 external to the transport or vehicle 104. Transports 104 andother transports 108 may include any type of self-propelled conveyance,including cars, motorcycles, trucks, construction equipment, or evenlocal single passenger transports 104, 108 such as SEGWAYs or similarconveyance. Transports 104, 108 may have a human driver or be driverlesstransports, and may or may not have any passengers. Transports 104, 108may include cargo transports including delivery vans, mail deliverytransports, and unmanned package delivery drones.

Later model transports 104 may include various computers, communicationdevices, and sensors. These resources collectively provide navigationfunctions, hands-free communication, parking assistance, collisiondetection, and monitoring of nearby other transports 108 in order toprovide more information and convenience to passengers and reduce theopportunity for impacts or accidents. These computers, communicationdevices, and sensors may communicate with other computers, communicationdevices, and sensors either within or outside of the transport 104through various technologies such as a transport's Controller AreaNetwork (CAN) bus, BLUETOOTH, WIFI, or the like.

Assume a pedestrian 114 may always have at least one device 118 on them.The device 118 may send information to a transport 104, or a transport104 may obtain information from the device 118. In some embodiments, asystem 100 may also determine a distance between the transport 104 and adevice 118 (e.g. mobile phone) and may determine characteristics of thedevice 118 such as moving in a motion suggesting it is in a person'spocket (i.e. moving left to right which may be universally determined tobe a cell phone in a pocket). Another device or processor may be awearable device, such as a smart watch, glasses, etc., which may have adifferent movement but can still be determined by movement analysis. Oneor more signals from these devices 118 may be captured by the transport104, including speed, direction, distance from the transport 104, etc.Based on this information, the system may create a “visual” of anindividual 114 associated with the device 118 and where they are headingin comparison to the transport 104. In one embodiment, a transport 104may take evasive action prior to an accident.

Consensus may mean taking more than one reading from multiple processorsassociated with an individual 114 associated with a device 118. Forexample, a dog's collar may include a processor, and a processor mayalso be present on the individual 114. Both devices may provideinformation to a transport 104. For example, a transport 1 (T1) 104 mayreceive data from a processor associated with a person or device user114. A transport 2 (T2) 104 may detect the same thing. A transport 3(T3) 104 may also detect the same thing. All three transports 104 may bein proximity to the same individual 114. T1, T2, and T3 may also beapproaching from different directions. For example, T3 104 may bestopped at a stop sign and T2 104 and T1 104 may be approaching fromdifferent directions. All data 120 may be sent to a server 112 (notshown), where consensus is derived that the individual 114 is moving atthat location, at a given speed and direction. Information may also besent to transports 104 that may coincide with the individual 114 at somepoint when a potentially dangerous situation may arise.

As another example, most cities have “walk” signs at crosswalks.Sometimes, a countdown is shown where a number of seconds are presentedwhen the pedestrian 114 should clear the road. A transport 104 in afurthermost lane from where the pedestrian 114 may be in a positionwhere there may arise a dangerous situation. The light turns green, andthe pedestrian 114 may still be in the road. The system 100 maydetermine, based on transports 104, 108 that are at the crosswalk, thatthe pedestrian 114 is not going to make it across the road in the timewhen the light turns green for the transports 104, 108. Based on thespeed that the pedestrian 114 is walking (such as the data received bythe at least one transport 104), a consensus may be made from othertransports 108 stopped at the crosswalk to validate the determination.For example, the system 100 may send a warning to a furthermosttransport 108, where the warning contains: “When light turns green,don't proceed. A pedestrian may be in the crosswalk”.

In an alternate embodiment, the system 100 may notify transports 104,108 that may at another time be involved in the dangerous situation,based on the received data. For example, if a person is riding a bicycledown a roadway, the system may send a notification to other transports108 behind the nearest transport 104, in addition to the nearesttransport 104, as those transports 108 may arrive at a dangeroussituation. In yet another embodiment, when a person on a scooter isgoing 20 mph and cars are going 20 mph, no notifications may be sent,since the scooter is going at or around a speed of the transports 104,108.

A device 118 may be associated with a device user 114, who is anindividual in proximity to the transport 104. The individual 114 may bea pedestrian in proximity to the transport 104, such as a pedestrian inor approaching a crosswalk or a roadway, or a device user 114 operatinga form of conveyance such as a scooter, a skateboard, roller skates, orother forms of conveyance.

A transport 104 may receive data 120 from the device 118 that is inproximity to the transport 104. The device data 120 may include one ormore of a speed, a direction, and a distance of the device 118 from thetransport 104. Next, the transport 104 determines a dangerous situationexists, based on analyzing the device data 120. For example, thetransport 104 may determine a dangerous situation exists because thedevice data 120 indicates insufficient reaction time exists for atransport 104, 108 to avoid a pedestrian 114 in a crosswalk, due tolimited visibility or night conditions. The transport 104 determines aconsensus in order to validate the dangerous situation. The consensusmay be determined from one or more other devices 116 proximate to thetransport 104, and/or one or more other transports 108 proximate to thetransport 104. FIG. 1A illustrates two other devices 116, shown as otherdevice 116A and other device 116B. After obtaining the consensus, thetransport 104 notifies 122 the device 118, where the notification 122includes the dangerous situation. The dangerous situation may includeany situation that places the device 118 and/or device user 114 in perilrelative to the transport 104 or another transport 108. For example, adangerous situation may include a crosswalk, a road shoulder, a babystroller, a wheelchair, or any type of situation that reduces a safetymargin relative to the transport 104, device 118, and device user 114.

In one embodiment, the transport 104 may transmit the data 120 to aserver 112 (not shown), which may determine the dangerous situation,obtain the consensus from one or more other devices 116 and/or one ormore other transports 108, and provide a notification of the dangeroussituation 122 to the transport 104 itself of the user device 118.Determining the dangerous situation may be based on a speed and adirection of the transport 104 and a speed and direction of the device118.

In another embodiment, a change in movement pattern of the device 118may be detected by analyzing the device data 120. The movement patternchange may reflect a change from a pedestrian speed and direction (e.g.2 miles per hour along a walking path parallel to a roadway) to atransport occupant speed and direction (e.g. 35 miles per hour in anopposite direction on the roadway). This detected change in movementpattern may trigger checking to see if an amber alert should begenerated. For example, a local or county police department could bechecked for a potential amber alert if a change in movement pattern weredetected. If a recently missing person were identified, the locationassociated with the change in movement pattern could be supplied tolocal or county police or the amber alert service, and various camerasor other devices in proximity to the location could be reviewed toprovide an individual or transport 104 identity. In one embodiment, anotification 122 may be provided to one or more other devices 116 inproximity to the user device 118 to initiate audio and/or videorecording.

In one embodiment, a transport 104 or other transport 108 may beprovided with an action to take in order to reduce the chance of thetransport 104 impacting a user 114 associated with the user device 118,or those close to the user device 118. In another embodiment, the device118 may receive the notification 122 and in response initiate audioand/or video recording.

FIG. 1B illustrates an example diagram of a transport system forprocessing primary and secondary data 130. Primary data 132 is fromdevices related to an event. Secondary data 134 is from devices fromtransports and/or people in proximity to the event. In one embodiment, aserver 112 receives primary data 132 related to an event from one ormore devices associated with one or more transports 104. The event mayinclude a collision, a near-collision, or a serious traffic infraction.The one or more devices may be individual devices associated withindividual occupants of the one or more transports 104. In otherembodiments, the one or more devices associated with the one or moretransports 104 may be embedded within, and as part of, the one or moretransports 104. The server 112 also receives secondary data 134 from oneor more other devices 116 proximate to the event. For example, there maybe other individuals not in the transport 104 but close by in the area.One or more of the individuals may have a device 116 that providessecondary data 134 to the server 112.

In one embodiment, an event may occur such as a transport 104 beingimpacted by another transport 108. The transport 104 and a deviceassociated with the other transport 108 may send data (i.e. primary data132) associated with the event to a server 112. Other transports 108and/or devices 116 may also send data (i.e. secondary data 134)associated with the event to the server 112. The server 112 analyzes thedata and sends the data associated with the event to other transports108 and/or devices 116 in proximity to the event. When the event isdetermined to be alleviated, the server 112 may send a firstnotification 136 to the entities associated with the primary data 132and a second notification 138 to the entities associated with thesecondary data 134.

An example of the instant solution includes receiving, by a server 112(which may be in a network 152, and/or on a transport 104, 108, etc.),primary data 132 (which may be video/image/text/statistical data fromsensors on a transport 104, 108) from at least one device 116 (which maya device in, on, or associated with a transport 104, a device 116 on abicyclist/jogger/pedestrian and/or a building, light pole and the like)related to an event (which may a near collision, a collision, a trafficinfraction, etc., between: a single transport 104 and an object, two ormore transports 104, 108, a person and a transport 104, etc), receiving,by the server 112 secondary data 134 (which may bevideo/image/text/statistical data of the event from anotherangle/distance/time) from other devices 116 proximate to the event(which may be devices 116 associated with pedestrians near the event,and/or devices in, on, or associated with transports 108 near theevent), analyzing, by the server 112, the primary data 132 and thesecondary data 134 to determine the severity of the event (which mayreflect an amount of damage, injury, road condition, etc.), and sending,from the server 112, a first notification 136 (which may includeinstructions on next steps, safety considerations based on the severity,etc.) to the at least one device associated with the primary data 132,and a second notification 138 (which may include a request foradditional information related to the event, directions to maneuveraround the event, etc.) to at least one of the other devices 116associated with the secondary data 134 based on the severity of theevent.

The server 112 may analyze the received primary 132 and secondary 134data in order to determine a severity of the event. Typically, primarydata 132 may be produced by occupant devices and reflects an internalstate of the transport 104, while secondary data 134 is produced bypedestrian or outside devices 116 and reflects an external state of thetransport 104. The internal state of the transport 104 may be moreuseful for determining any injuries to occupants, while the externalstate of the transport 104 may be more useful for determining damage tothe transport 104 itself or any property damage sustained. Afterdetermining the severity of the event, the server 112 may send a firstnotification 136 to the one or more devices associated with the primarydata 132 and a second notification 138 to one or more of the otherdevices 116 associated with the secondary data 134. One or both of theprimary 132 and secondary 134 data may include one or more of audio,video, images, text, and statistical data. The secondary data 134 mayreflect a different direction, perspective, or distance than the primarydata 132.

In one embodiment, the server 112 may transmit a final notification tothe one or more devices and other devices when the event is alleviated.This final notification may be intended to let a user associated withthe devices or other devices that the event or event severity are nolonger of concern. In one embodiment, in response to the server 112determines a severity for the event, one or more types of firstresponders associated with the severity may be selected. A thirdnotification may be provided to selected first responders in order torespond to the event. For example, the severity of the event mayindicate the presence of fire, and the third notification may requestfiremen as first responders.

In one embodiment, a line of sight may be determined to not existbetween the one or more other devices 116 providing the secondary data134 and the one or more devices associated with the one or moretransports. In one embodiment, each other device 116 may capture apanoramic image of the area in proximity to the other device 116.Panoramic images may be analyzed to determine if the panoramic imagesinclude the transport 104; if the panoramic images do not include thetransport 104, then a line of sight does not exist between the one ormore other devices 116 providing the secondary data 134 and the one ormore devices associated with the one or more transports. In some cases,this may result in the secondary data 134 from the one or more devices116 not being included in the severity of event analysis. One reasonthis may not occur may be related to security: not including secondarydata 134 that has not been vetted or at least have the credibility ofbeing related in some way to the primary data 132. Another reason thismay not occur may be related to one or multiple events. For example, alack of line of sight may indicate a first event from the primary data132 and a different, unrelated second event from the secondary data 134.Ideally, each of these two or more events could require a separate andindependent analysis/evaluation, rather than including the secondarydata 134 in the event severity analysis for the primary data 132. Inthat case, the secondary data 134 may be only used for analysis of asecond event and determining a second event severity.

In one embodiment, the server 112 may analyze timestamps associated withthe primary data 132 and secondary data 134 in order to determine aseverity of the event. Each of the primary 132 and secondary 134 datamay include timestamps that reflect a time when the primary data 132 orthe secondary data 134 was created. The server 112 may review thedifference between timestamps for the primary 132 and secondary 134 datato check if the difference is within a predetermined range. For example,if timestamps fall within the predetermined range, it may indicatetimestamps associated with the same event. If timestamps do not fallwithin the predetermined range, it may indicate timestamps associatedwith different events.

FIG. 1C illustrates an example diagram of a transport system forresolving and improving near accident conditions 140. A transport system140 may include a server 112 coupled to a network 152, a transport orvehicle 104, another transport 108, and one or more devices external tothe transport 150. A near-accident 146 is an event that comes close toan accident without there being an actual impact between two or moretransports 104, devices 150, individuals, or property.

Accidents that occur between transports 104 and pedestrians are normallyrecorded, such as in police reports—especially when an in injury occursas a result of an accident. Near-accidents 146 are not normallyrecorded, as there is no current method to record a near-accident 146.When a transport 104 almost hits a pedestrian, bicyclist, a person on ascooter, a jogger, or the like, both the transport 104 and/or the personexternal to the transport 104 may not even interact with each other.What is needed is a method to record the near-accident 146 to a server112 or database, and an ability to perform statistics of near-accidents146 to be able to determine dangerous locations 144. Thesedeterminations may be useful to entities responsible for making roadssafe, such as the cutting of trees and bushes near a road, addingadditional road signs to warn people and transports 104 of a potentiallydangerous location 144, improving the line of sight, improve lighting,and the like. When a safer environment is unable to be created,transports 104 and/or devices associated with people outside oftransports 150 learn to maneuver in a different manner that is deemed tobe safer, such as through notifications received by the system 140 whenthey are at or near a dangerous location 144. Additionally, data 142 maybe useful for urban planning, event planning, and provide the ability toaccess a situation and/or memorialize a situation such as anear-accident 146.

In one embodiment, a near-accident 146 may be defined by a maximum rangeapart that the two objects pass within each other without touching, forexample, six feet. For example, a near-accident 146 may be registered ifa sensor on a transport 104 measures a range to another object usingradar, lidar, or other sensing technology. In another embodiment, anear-accident 146 may be defined by a maximum time apart that two occupya same space or location. For example, a second transport 108 in thesame space as a first transport 104 and less than two seconds later maybe defined as a near-accident 146. The time delta defining a nearaccident 146 may also be dependent on a speed and/or acceleration of oneor more transports 104, or a relative speed between one or more objects.A server 112 may be tracking position, location, speed, direction, andacceleration between all types of objects, and continually updatingnear-accident 146 data with such occurrences as they occur. In this way,a dynamic database of near-accident 146 occurrences may be continuallyupdated. Objects having frequent occurrences of near-accidents 146 maybe identified and logged, with other objects notified in advance ofincreasing proximity to such objects. In some embodiments, objectshaving frequent occurrences of near-accidents 146 may be moving objectssuch as transports 104 or other transports 108, or conveyance associatedwith devices 150. In other embodiment, objects having frequentoccurrences of near-accidents 146 may be static and unmoving objectssuch as dangerous locations 144 or a terrain feature such as a tree, abridge, or a concrete abutment.

Referring to FIG. 1C, a transport 104 is depicted on a roadway wherethere may be obstacles near the road, for example. The obstacles shownin FIG. 1C are bushes near the roadway. A bicyclists in front of thetransport 104. The bicyclist has in possession a device 150, such as amobile device. The transport 104 and the device 150 may be communicablycoupled to a network 152, such as the Internet. Also communicablycoupled to the network 152 is a server 112. Additional elements may bepresent that are part of the system 140: an external device, such as aman walking a dog, or another transport 108 on the roadway traveling inthe opposite direction of the transport 104, for example. The man and/orthe dog may have another device 150 that is connected to the network152, and the transport 108 may also have a connection to the network152.

As the transport 104 comes near the bicyclist in a near-accident 146scenario, first data 142 is sent from the transport 104 and thebicyclist device 150 to the server 112 via the network 152. The data 142may be sent from a computer in the transport 104, such as a transportcomputer. Data 142 may also be sent from the device 150 to the server112 via the network 152. In one embodiment, first data 142 may be sentwhen a maneuver is detected by sensors on the transport 104, such as asharp turn, sharp braking, or the like. Additionally, first data 142 maybe sent by a device 150 associated with the bicyclist, such as a mobiledevice 150, when a maneuver is detected by sensors on the device 150,such as sharp turning, sharp braking, or the like. In anotherembodiment, first data 142 may be sent from the transport 104 and thedevice 150 when an abrupt maneuver of one or more of the transport 104and the device 150 is paired with the two computers at a close range,such as a range below a determined threshold. The first data 142 sent tothe server 112 may include details of the near-accident 146 and mayinclude but not be limited to the location of the computer, the time ofthe near-accident 146, the speed at the time of the near-accident 146, adirection of the computer at the time of the near-accident 146, and arate of deceleration of the computer at the time of the near-accident146. The server 112 may be able to ascertain from stored data the numberof near-accidents 146 at the location of the current near-accident 146to determine whether the location is considered a dangerous location144, based on the number of historical near-accidents 146 at thatlocation. This data may be stored in the server 112, such as in adatabase (not shown). When a location is determined to be a dangerouslocation 144, the system may take further action. A notification 148 maybe sent by the server 112 to one or more external devices proximate tothe location 150, such as the man walking the dog and another transport108, for example. The notification 148 may include information about thedangerous location 144. This may allow other device in proximity 150 tobecome aware of the dangerous location 144 and possibly avoid anunlikely event. The computers may receive the notification 148 and mayperform an alert in procedures normally utilized for incomingnotifications. Other devices 150 and transports 108 whose speed anddirection may indicate that they will be in proximity to the dangerouslocation 144 according to the system 140, may also be notified of thedangerous location 144.

In one embodiment, external devices 150, such as the other transport108, the device 150, the transport 104, and the server 112, may be incommunication with one another, such as via the network 152. In anotherembodiment, the server 112 and one or more elements in a telecom networkmay be in communication with one another herein the at least oneexternal device 150, or additional external devices 150 (not shown) maybe part of the telecom network 152. The transport 104 may sendinformation to the server 112, wherein a telecom network 152 such asVerizon is communicated with, such as a query with data indicating arequest for the transport's 104 location at a given time. The Verizonnetwork may respond to the query with the data. In another embodiment,when the server 112 may receive information from the transport 104regarding the near-accident 146, the server 112 may send a query to anetwork 152, such as the Verizon network, requesting which devices wereclosest to the transport 104 at the location at the time of thenear-accident 146. A response from the Verizon network (for example) mayallow the server 112 then to know the devices external to the transport150 where interaction may occur, such as notifications 148 to thedevices 150. In another embodiment, a server 112 may perform analysisaccording to received data. For example, the server 112 may be able toascertain the type of transport 104 associated with the external device150. According to the speed and location of the external device 150, theserver 112 may be able to determine if the device 150 is associated witha person walking, a person on a scooter, a person on a bike, a personjogging, etc. Therefore, the server 112 is able to determine that therewas a scooter at the location due to the location and the speed of thedevice 150. The server 112 is then able to determine that scooters maybe banned from that location when there are a number of near-accidents146 of scooters at that location.

In another embodiment, data 142 received at the server 112 from theexternal device 150 may include the location of the device 178, wherethe location is one or more a location of the transport 104 and alocation of the device 144. In another embodiment, the server 112 maysend a notification 148 to another computer connected to the network 152(not shown) containing information about the dangerous location 144.This may be used to correct elements causing the dangerous location 144.For example, an entity responsible for road maintenance, the entityresponsible for trimming bushes and/or trees near a road, and the like.

In another embodiment, the notification 148 sent to the devices externalto the transport 150 may include steps to avoid the dangerous location144. In another embodiment, both the transport 104 and the device 150associated with the near-accident 146 may have an application downloadedon and executing on the respective computer, the application mayimmediately, provides information about the dangerous location 144. Ifeither the device 150 or transport 104 do not have the downloadedapplication, the server 112 may become involved.

The server 112 receives first data 142 from a transport 104 thatexperiences a near-accident 146 with a device external to the transport150. The device external to the transport 150 may be associated with apedestrian or individual in close proximity to a dangerous location 144,and may be a smart phone, smart watch, wearable computer, or other formof communication device associated with the pedestrian or individual.The first data 142 may include a location of the transport 104 andinformation related to maneuvering of the transport 104. The maneuveringof the transport 104 constitutes the near-accident 146 based on thefirst data 142. Next, the server 112 determines that the location of thetransport 104 is a dangerous location 144 from the first data 142.Finally, the server 112 sends a first notification 148 to anothertransport 108 proximate to the dangerous location 144. The firstnotification 148 includes information related to the dangerous location144 and the near-accident 146. The information related to the dangerouslocation 144 may include a recommended item to reduce futurenear-accidents at the dangerous location 144.

The first data 142 may include a location of the transport 104 and oneor more of a time at the location of the device 150, a time at thelocation of the transport 104, a speed of the device 150, a speed of thetransport 104, a direction of the device 150, a direction of thetransport 104, a deceleration rate of the device 150, a decelerationrate of the transport 104, more than a threshold number of first data142 instances at the location of the transport 104 previously receivedby the server 112, a swerving rate of the transport 104, a honking of ahorn of the transport 104, a flashing of headlights of the transport104, an engagement of taillights of the transport 104, etc.

In one embodiment, the server 112 may determine any other devices 150 inproximity to the transport 104 when the near-accident 146 occurred,obtain second data (not shown) from the other devices 150, and modifythe notification 148 based on the second data. In another embodiment,the server 112 may send a second notification (not shown) to one or moreentity devices, where the one or more entity devices may be responsiblefor alleviating the dangerous condition.

In one embodiment, the transport 104 experiencing the near-accident 146and the device external to the transport 150 may each include anapplication configured to autonomously communicate the first data 142 tothe other of the transport 104 and device 150. In another embodiment,the server 112 may analyze the first data 142 and determine a highnear-accident 146 rate associated with a type of conveyance associatedwith the device 150 (a bicycle, for example). The server 112 may theninclude a restriction on operation of the type of conveyance in thefirst notification 148 (e.g., bicycles may only be operated duringdaylight hours in proximity to the dangerous location 144).

In another embodiment, the server 112 may receive less than a thresholdnumber of first data 142 instances at the location of the transport 104previously received by the server 112. The threshold number may indicatea minimum number of reported first data 142 instances required toanalyze to determine a location may be a dangerous location 144. In theevent a threshold number of first data 142 instances has not beenreceived, the server 112 may not send the first notification 148 andinstead increment the number of first data 142 instances. This will movethe number of first data 142 instances closer to the threshold number sothat a next first data 142 instance may possibly trigger the thresholdand first notification 148.

FIG. 1D illustrates an example diagram of a transport system forresolving item dispositions in response to a dangerous situation 160,according to example embodiments. Referring to FIG. 1D, a plan view 160of an exemplary transport or vehicle 104 is shown. Transports orvehicles 104 may generally be classified as passenger transports orcargo transports. Passenger transports are primarily intended totransport passengers, while cargo transports are primarily intended totransport cargo.

Any type of transport or vehicle 104 that may carry one or more items162 applies to FIG. 1D. Items 162 are any physical objects that are notpart of the transport or vehicle 104 and are not physically attached toan interior of the transport or vehicle 104. Items 162 may include anycargo, bags, purses, tools, pets, and/or computing or communicationdevices 164. Computing and communication devices 164 are generallyassociated with specific occupants of the transport or vehicle 104. Insome embodiments, occupants may be considered items 162 if not securelybelted into the transport or vehicle 104.

Transport or vehicle 104 may include any number of passenger seats. Eventhough passenger transports 104 are primarily intended to carrypassengers, all such transports 104 may carry many items 162, dependingon size. Some transports 104 include a trunk area to carry items 162separate from the passenger cabin, and the present application is notintended to address items stored in a trunk, glove compartment, console,or other such enclosed areas separate from a passenger compartment.Those enclosed areas typically protect transport 104 occupants fromunsecured items 162 moving in an uncontrolled manner.

Item 162 management is important to efficient and safe operation oftransports 104. Heavy and/or poorly distributed items 162 may place anuneven load on a transport 104. For example, an excess of weight may beon the right side vs. left side or the back vs the front. This maynegatively impact gas mileage, brake wear, tire wear, or transport 104performance. Transport 104 performance may include handling, cornering,acceleration, or braking—and in extreme unbalanced situations may makeit easier for the transport 104 to roll over or lose control in anaccident or during evasive maneuvers. Unsecured items 162 may becomeprojectiles during transport 104 movement, and potentially injure ordistract the driver or other occupants—either of which may cause atransport 104 accident.

Any number of items 162 may be carried within a passenger compartment ofa transport 104. FIG. 1D shows many items 162 placed into a rear cargocompartment of a transport 104 such as an SUV or minivan. One computeror communication device 164 (also an item 162) has been placed on a2^(nd) row passenger seat, and another item 162 has been placed on thedashboard in front of the Pt row seats. The transport or vehicle 104 mayinclude one or more interior cameras 168 or other sensors, and mayprovide audible notifications 170 to the driver or other occupants ofthe transport 104. The transport 104 may also send visual and/or audiblenotifications to occupant computing or communication devices 164, orpresent visual notifications through a display of the transport 104 (notshown). The other sensors may include any number of pressure or othersensors that can detect items 162 in various specific locations withinthe transport 104. Such sensors may, for example, detect pressure inmany discrete areas within the transport 104 and determine whether ornot items 162 have been loaded in a balanced fashion.

In one embodiment, a transport or vehicle 104 may experience a dangeroussituation. Dangerous situations may include any transport 104 failure,an unsecured item 162, an unsafe item 162 position, an unsafe item 162,or historical data associated with a dangerous location in proximity tothe transport. Transport 104 failures may include any type ofmechanical, electrical, hydraulic, software, or visibility—relatedfailures. Unsecured items 162 are any items 162 that may potentiallybecome uncontrolled projectiles within the transport 104 cabin. Anunsafe item 162 position includes any location within a transport 104that is not intended for the item 162 to be—such as on a lap or on adashboard. An unsafe item 162 is an item that may be unsafe totransport, including but not limited to flammable liquids, cutlery,loaded and unsecured firearms, pointed objects, radioactive objects, andbreakable objects.

The transport 104 determines a number of items 162 in the transport 104.In one embodiment, one or more cameras 168 within the transport 104obtain camera images of the interior of the transport 104. Each suchcamera 168 may have a different location and viewing perspective fromall other cameras 168 within the transport 104. Each of the obtainedcamera 168 images is then compared to stored images of the transport104. The stored images reflect the appearance of the transport 104 whenit is empty. A computer or processor within the transport 104 mayperform the comparison, using stored images in a memory deviceaccessible to the computer or processor. The computer or processor mayperform edge detection, item 162 surface reflection or brightness, orutilize other optical techniques to identify each separate item 162.

The computer or processor determines differences between the cameraimages and the stored images, and may establish the number ofdifferences as the number of items 162. Images from a given camera 168may be cross-referenced with images from other cameras 168 to correlateeach item 162 and ensure that a same item 162 is not counted multipletimes. Alternately or in combination with optical comparison usingcameras 168, the transport 104 may utilize one or more pressure or othertypes of sensors to identify and discriminate items 162 to arrive at anitem 162 number.

The transport 104 may also determine a position of each item 162 in thetransport 104. In one embodiment, the one or more cameras 168 may obtaincamera images of the interior of the transport 104. Each such camera 168may have a different location and viewing perspective from all othercameras 168 within the transport 104. The computer or processor mayidentify one or more items 162 from the camera images, and map each ofthe one or more items 162 to location data of the transport interior.For example, the transport memory may include location data for eachdiscrete position within an empty transport 104, to a level ofgranularity. By mapping each item 162 to the stored location data, aposition of each item 162 may be determined.

In one embodiment, the transport 104 may determine a dangerous situationand responsively provide a safety notification 170 associated with oneor more items 162 (after number and position of the items 162 has beendetermined. In one embodiment, the dangerous situation may includeanother transport operating in an unsafe manner in proximity to thetransport 104. An unsafe manner may include swerving repeatedly,entering a roadway lane of the transport 104, or other similar behavior.In response to the dangerous situation, the transport 104 may provide asafety notification 170. The safety notification 170 may include one ormore of a request for occupants of the transport to put down handhelddevices, pay attention to surroundings of the transport, and adescription of the dangerous situation. The safety notification 180 mayinclude an audible notification, a visual notification, a vibrationnotification, or any combination or other type of notification.

In one embodiment, the transport 104 may determine the dangeroussituation within a time period, and take one of several actions. In oneembodiment, it may inhibit the transport 104 from starting until thedangerous situation is resolved, if the computer or processor determinesthe transport 104 is not started. It may inactivate the transport 104 ifthe computer or processor determines the transport 104 is started andparked. Finally, it may provide a second safety notification if thecomputer or processor determines the transport 104 is started and notparked.

In one embodiment, one or more sensors within the transport 104 mayobtain a current position and weight of each item 162 in the transport104, determine a weight distribution of the items 162, and provideinstructions to move one or more items 162 to achieve a balanced weightdistribution for the items in the transport 104. This may utilize one ormore pressure sensors, as previously discussed.

FIG. 1E illustrates an example diagram of a transport system for takingactions based on received atypical data 180, according to exampleembodiments. Referring to FIG. 1E, a system 180 for receiving andprocessing atypical data associated with an exemplary transport orvehicle 104 is shown.

The system 180 may include one or more transports or vehicles 104 andone or more other transports 108. A transport 104 may be communicativelycoupled to a server 112, and receive notifications 186 as describedherein. One or more device users 114 with corresponding other devices116 may be present. FIG. 1E illustrates two device users, identified asdevice users 114A and 114B. Device user 114A is associated with otherdevice 116A, and device user 114B is associated with other device 116B.In addition to device users 114, other devices may be present as well,including a device associated with one or more other transports 108 anda fixed camera 184. The devices 116, 184 and devices associated withother transports 108 may be either stationary or in motion.

The transport or vehicle 104 may experience an event, which may includean accident or collision with the other transport 108 or property, orthe transport 104 being struck by an object. The event may also includea failure of the transport 104, including but not limited to a loss ofautomotive fluids and catching fire. The server 112 may determine theevent associated with the transport 104.

Prior to the event, the server 112 may receive atypical data 182 relatedto the transport 104 from a plurality of devices 116, 184 over varioustimes prior to the event. FIG. 1E illustrates atypical data 182Aprovided by other device 116A, atypical data 182B provided by otherdevice 116B, and atypical data 182C provided by camera 184. The atypicaldata 182 may include any of audio, video, photos, SMS messages, emails,or sensor data that communicates anomalous behavior in proximity to thetransport 104. In one embodiment, the atypical data 182 may includesensor data relating to anomalous behavior of the transport 104 from theplurality of devices 108, 116, 184. For example, other device 116A maycommunicate one or more photos showing transport 104 leaking automotivefluids prior to an engine of transport 104 catching fire as atypicaldata 182A. For another example, a camera 184 associated with a trafficsignal may communicate video and audio showing other transport 108speeding or driving erratically prior to running into the rear oftransport 104 as atypical data 182C.

The server 112 may receive atypical data 182 from multiple sources, andanalyzes the atypical data 182. The analysis may attempt to determinesimilarities between each of the atypical data 182 received, for examplebetween atypical data 182A and 182B, between atypical data 182B and182C, and between atypical data 182A and 182C. Because differentatypical data 182A, 182B, 182C may include different types of data (i.e.audio, video, photos, SMS messages, emails, or sensor data, etc) fromdifferent locations and angles, analysis by the server 112 may includecorrelation of the different atypical data 182. For example, if atypicaldata 182A indicates a collision between other transport 108 andtransport 104, atypical data 182B and 182C should be similarly reviewedand analyzed for correlated evidence of a same collision. In oneembodiment, the analyzed data associated with each of the plurality ofdevices 108, 116, 184 may be over a minimum threshold, where the minimumthreshold may reflect one or more of a degree of damage to the transport104 such that the transport 104 is not likely able to move on its ownand there are likely injuries to one or more transport 104 occupants. Inanother embodiment, analyzing the atypical data 182 may include theserver 112 determining a state of the transport 104 based on datacollected by each device of the plurality of devices 108, 116, 184 at aparticular time of the various times. The state may be related to theevent, and a degree of atypical data 182 may relate to the severity ofthe event.

The server 112 next forms a consensus based on the analyzed atypicaldata 182 to determine a severity of the event. In one embodiment, theconsensus may be established from identical or similar analysis resultsfrom a majority of atypical data 182A, 182B, 182C. In anotherembodiment, the consensus may be based on a majority of the plurality ofdevices 108, 116, 184 providing atypical data 182 within a common range,where the common range may correspond to the severity of the event. Inanother embodiment, a blockchain network may include the server 112,transport or vehicle 104, other transport 108, other devices 116A, 116B,and camera 184. The server 112 may create and store a blockchaintransaction related to the event in a blockchain ledger. The blockchaintransaction may be based on one or more of the received data, theanalyzed data, a location of the plurality of devices, a type of theplurality of devices, the various times, a type of a severity of theevent, and/or a type of an action to take.

In one embodiment, after forming the consensus the server 112 maydetermine an action to take based on the severity of the event. Inanother embodiment utilizing a blockchain network, the server 112 mayexecute a smart contract between the server 112 and the transport 104related to the action to take. In another embodiment, the server 112 mayprovide a notification 186 to the transport 104 in response to theseverity of the event is low, provide a notification 186 to theplurality of devices 108, 116, 184 in response to the severity of theevent is moderate, and/or contact 186 one or more first responders inresponse to the severity of the event is high. In one embodiment, theserver 112 may send the action to take to the transport 104 based on theseverity, of the event. In another embodiment, the server 112 may sendthe action to take to the plurality of devices 108, 116, 184 and/or adevice associated with the transport 104 or an occupant of the transport104.

FIG. 2A illustrates a transport network diagram 200, according toexample embodiments. The network comprises elements including atransport node 202 including a processor 204, as well as a transportnode 202′ including a processor 204′. The transport nodes 202, 202′communicate with one another via the processors 204, 204′, as well asother elements (not shown) including transceivers, transmitters,receivers, storage, sensors and other elements capable of providingcommunication. The communication between the transport nodes 202, 202′can occur directly, via a private and/or a public network (not shown) orvia other transport nodes and elements comprising one or more of aprocessor, memory, and software. Although depicted as single transportnodes and processors, a plurality of transport nodes and processors maybe present. One or more of the applications, features, steps, solutions,etc., described and/or depicted herein may be utilized and/or providedby the instant elements.

FIG. 2B illustrates another transport network diagram 210, according toexample embodiments. The network comprises elements including atransport node 202 including a processor 204, as well as a transportnode 202′ including a processor 204′. The transport nodes 202, 202′communicate with one another via the processors 204, 204′, as well asother elements (not shown) including transceivers, transmitters,receivers, storage, sensors and other elements capable of providingcommunication. The communication between the transport nodes 202, 202′can occur directly, via a private and/or a public network (not shown) orvia other transport nodes and elements comprising one or more of aprocessor, memory, and software. The processors 204, 204′ can furthercommunicate with one or more elements 230 including sensor 212, wireddevice 214, wireless device 216, database 218, mobile phone 220,transport node 222, computer 224, I/O device 226 and voice application228. The processors 204, 204′ can further communicate with elementscomprising one or more of a processor, memory, and software.

Although depicted as single transport nodes, processors and elements, aplurality of transport nodes, processors and elements may be present.Information or communication can occur to and/or from any of theprocessors 204, 204′ and elements 230. For example, the mobile phone 220may provide information to the processor 204, which may initiate thetransport node 202 to take an action, may further provide theinformation or additional information to the processor 204′ which mayinitiate the transport node 202′ to take an action, may further providethe information or additional information to the mobile phone 220, thetransport node 222, and/or the computer 224. One or more of theapplications, features, steps, solutions, etc., described and/ordepicted herein may be utilized and/or provided by the instant elements.

FIG. 2C illustrates yet another transport network diagram 240, accordingto example embodiments. The network comprises elements including atransport node 202 including a processor 204 and a non-transitorycomputer readable medium 242C. The processor 204 is communicably coupledto the computer readable medium 242C and elements 230 (which weredepicted in FIG. 2B).

The processor 204 performs one or more of the following steps. At step244C, a transport 104 receives data 120 from a device 118 in proximityto the transport 104. At step 246C, the transport 104 determines adangerous situation from the data 120. At step 248C, the transport 104obtains consensus to validate the dangerous situation. Finally, at step250C, the transport 104 notifies the device 118 about the dangeroussituation 122.

FIG. 2D illustrates a further transport network diagram 260, accordingto example embodiments. The network comprises elements including atransport node 202 including a processor 204 and a non-transitorycomputer readable medium 242D. The processor 204 is communicably coupledto the computer readable medium 242D and elements 230 (which weredepicted in FIG. 2B).

The processor 204 performs one or more of the following steps. At step244D, a server 112 receives primary data 132 from one or more devicesassociated with the transport 104. At step 246D, the server 112 receivessecondary data 134 from one or more other devices 116. At step 248D, theserver 112 analyzes the primary 132 and secondary 134 data in order todetermine a severity of an event. Finally, at step 252D, the server 112sends a first notification 136 to one or more devices and a secondnotification 138 to one or more other devices 116 based on the eventseverity.

FIG. 2E illustrates a yet further transport network diagram 270,according to example embodiments. The network comprises elementsincluding a transport node 202 including a processor 204 and anon-transitory computer readable medium 242E. The processor 204 iscommunicably coupled to the computer readable medium 242E and elements230 (which were depicted in FIG. 2B).

The processor 204 performs one or more of the following steps. At step244E, a server 112 receives first data 142 from a transport 104 about anear accident 146 with a device external to the transport 150. At step246E, the server 112 determines a transport location may be dangerous144 based on the first data 142. Finally, at step 248E, the server 112sends a notification 148 to another transport 108 proximate to thedangerous location 144.

FIG. 2F illustrates a yet further transport network diagram 280,according to example embodiments. The network comprises elementsincluding a transport node 202 including a processor 204 and anon-transitory computer readable medium 242F. The processor 204 iscommunicably coupled to the computer readable medium 242F and elements230 (which were depicted in FIG. 2B).

The processor 204 performs one or more of the following steps. At step244F, a transport 104 determines a number of items 162, 164 in thetransport 104. At step 246F, the transport 104 determines a position foreach of the items 162, 164. At step 248F, a dangerous situationassociated with the transport 104 occurs. Finally, at step 250F, thetransport 104 provides a safety notification associated with one or moreof the items 162, 164. The safety notification may include one or moreof an audible warning 170 from the transport 104, a text messagereceived by an occupant device 164, and a warning shown on a transport104 display. In some embodiments, the transport 104 determines thenumber and position of items and provides the safety notification priorto the transport 104 is started.

FIG. 2G illustrates a yet further transport network diagram 290,according to example embodiments. The network comprises elementsincluding a transport node 202 including a processor 204 and anon-transitory computer readable medium 242G. The processor 204 iscommunicably coupled to the computer readable medium 242G and elements230 (which were depicted in FIG. 2B).

The processor 204 performs one or more of the following steps. At step244G, a server 112 determines an event associated with a transport 104.At step 246G, the server 112 receives atypical data 182 related to thetransport 104 from a plurality of devices 116 over various times priorto the event. At step 248G, the server 112 analyzes the atypical data182. At step 250G, the server 112 forms a consensus based on theanalyzed atypical data 182 in order to determine a severity of theevent. Finally, at step 252G, the server 112 determines an action totake based on the severity.

The processors and/or computer readable media may fully or partiallyreside in the interior or exterior of the transport nodes. The steps orfeatures stored in the computer readable media may be fully or partiallyperformed by any of the processors and/or elements in any order.Additionally, one or more steps or features may be added, omitted,combined, or performed at a later time, etc.

FIG. 3A illustrates a flow diagram 300, according to exampleembodiments. Referring to FIG. 3A, the process includes one or more ofthe following steps. At block 302, the transport 104 receives data 120from a device 118 in proximity to the transport 104. The data 120 mayinclude one or more of a speed, a direction, and a distance of thedevice 118 from the transport 104. At block 304, the transport 104determines a dangerous situation from the data 120. At block 306, thetransport 104 obtains a consensus to validate the dangerous situationfrom one or more other devices 116 proximate to the transport 104 andone or more other transports 108 proximate to the transport 104.Finally, at block 308, the transport 104 notifies 122 the device 118,based on the consensus, about the dangerous situation.

FIG. 3B illustrates another flow diagram 320, according to exampleembodiments. Referring to FIG. 3B, the process includes one or more ofthe following steps. At block 322, a server 112 receives primary data132 related to an event from one or more devices associated with one ormore transports 104. The one or more transports 104 may include thedevices inherently, or the devices may be associated with one or moretransport 104 occupants. At block 324, the server 112 receives secondarydata 134 from other device(s) 116 in proximity to the event. At block326, the server 112 analyzes the primary 132 and secondary 134 data todetermine an event severity. Finally, at block 328, the server 112 sendsa first notification 136 to the device(s) associated with the primarydata 132 and a second notification 138 to the other device(s) 116associated with the secondary data 134, based on the event severity.

FIG. 3C illustrates yet another flow diagram 330, according to exampleembodiments. Referring to FIG. 3C, the process includes one or more ofthe following steps. At block 332, a server 112 receives first data 142from a transport 104 experiencing a near-accident 146 with a deviceexternal to the transport 150. The first data 142 may include a locationof the transport 104 and information related to maneuvering of thetransport 104. At block 334, the server 112 determines the transportlocation is dangerous 144 and maneuvering of the transport 104constitutes the near accident 146, based on the first data 142. Finally,at block 336, the server 112 sends a first notification 148 to anothertransport 108 proximate to the dangerous location 144. The firstnotification 148 includes information related to the dangerous location144 and the near accident 146.

FIG. 3D illustrates yet another flow diagram 340, according to exampleembodiments. Referring to FIG. 3D, the process includes one or more ofthe following steps. At block 342, a transport 104 determines a numberof items 162, 164 in the transport 104. At block 344, the transport 104determines a position for each of the items 162, 164. At block 346, adangerous situation associated with the transport 104 occurs. Finally,at block 348, the transport 104 provides a safety notificationassociated with one or more of the items 162, 164. The safetynotification may include one or more of an audible warning 170 from thetransport 104, a text message received by an occupant device 164, and awarning shown on a transport 104 display. In some embodiments, thetransport 104 determines the number and position of items and providesthe safety notification prior to the transport 104 is started.

FIG. 3E illustrates yet another flow diagram 350, according to exampleembodiments. Referring to FIG. 3E, the process includes one or more ofthe following steps. At block 352, a server 112 determines an eventassociated with a transport 104. At block 354, the server 112 receivesatypical data 182 related to the transport 104 from a plurality ofdevices 116 over various times prior to the event. At block 356, theserver 112 analyzes the atypical data 182. At block 358, the server 112forms a consensus based on the analyzed atypical data 182 in order todetermine a severity of the event. Finally, at block 360, the server 112determines an action to take based on the severity.

FIG. 4 illustrates a machine learning transport network diagram 400,according to example embodiments. The network 400 includes a transportnode 402 that interfaces with a machine learning subsystem 406. Thetransport node includes one or more sensors 404.

The machine learning subsystem 406 contains a learning model 408 whichis a mathematical artifact created by a machine learning training system410 that generates predictions by finding patterns in one or moretraining data sets. In some embodiments, the machine learning subsystem406 resides in the transport node 402. In other embodiments, the machinelearning subsystem 406 resides outside of the transport node 402.

The transport node 402 sends data from the one or more sensors 404 tothe machine learning subsystem 406. The machine learning subsystem 406provides the one or more sensor 404 data to the learning model 408,which returns one or more predictions. The machine learning subsystem406 sends one or more instructions to the transport node 402 based onthe predictions from the learning model 408.

In a further embodiment, the transport node 402 may send the one or moresensor 404 data to the machine learning training system 410. In yetanother embodiment, the machine learning subsystem 406 may sent thesensor 404 data to the machine learning subsystem 410. One or more ofthe applications, features, steps, solutions, etc., described and/ordepicted herein may utilize the machine learning network 400 asdescribed herein.

FIG. 5A illustrates an example vehicle configuration 500 for managingdatabase transactions associated with a vehicle, according to exampleembodiments. Referring to FIG. 5A, as a particular transport/vehicle 525is engaged in transactions (e.g., vehicle service, dealer transactions,delivery/pickup, transportation services, etc.), the vehicle may receiveassets 510 and/or expel/transfer assets 512 according to atransaction(s). A transport processor 526 resides in the vehicle 525 andcommunication exists between the transport processor 526, a database530, a transport processor 526 and the transaction module 520. Thetransaction module 520 may record information, such as assets, parties,credits, service descriptions, date, time, location, results,notifications, unexpected events, etc. Those transactions in thetransaction module 520 may be replicated into a database 530. Thedatabase 530 can be one of a SQL database, an RDBMS, a relationaldatabase, a non-relational database, a blockchain, a distributed ledger,and may be on board the transport, may be off board the transport, maybe accessible directly and/or through a network, or be accessible to thetransport.

FIG. 5B illustrates an example vehicle configuration 550 for managingdatabase transactions conducted among various vehicles, according toexample embodiments. The vehicle 525 may engage with another vehicle 508to perform various actions such as to share, transfer, acquire servicecalls, etc. when the vehicle has reached a status where the servicesneed to be shared with another vehicle. For example, the vehicle 508 maybe due for a battery charge and/or may have an issue with a tire and maybe in route to pick up a package for delivery. A transport processor 528resides in the vehicle 508 and communication exists between thetransport processor 528, a database 554, a transport processor 528 andthe transaction module 552. The vehicle 508 may notify another vehicle525 which is in its network and which operates on its blockchain memberservice. A transport processor 526 resides in the vehicle 525 andcommunication exists between the transport processor 526, a database530, the transport processor 526 and a transaction module 520. Thevehicle 525 may then receive the information via a wirelesscommunication request to perform the package pickup from the vehicle 508and/or from a server (not shown). The transactions are logged in thetransaction modules 552 and 520 of both vehicles. The credits aretransferred from vehicle 508 to vehicle 525 and the record of thetransferred service is logged in the database 530/554 assuming that theblockchains are different from one another, or, are logged in the sameblockchain used by all members. The database 554 can be one of a SQLdatabase, an RDBMS, a relational database, a non-relational database, ablockchain, a distributed ledger, and may be on board the transport, maybe off board the transport, may be accessible directly and/or through anetwork.

FIG. 6A illustrates a blockchain architecture configuration 600,according to example embodiments. Referring to FIG. 6A, the blockchainarchitecture 600 may include certain blockchain elements, for example, agroup of blockchain member nodes 602-606 as part of a blockchain group610. In one example embodiment, a permissioned blockchain is notaccessible to all parties but only to those members with permissionedaccess to the blockchain data. The blockchain nodes participate in anumber of activities, such as blockchain entry addition and validationprocess (consensus). One or more of the blockchain nodes may endorseentries based on an endorsement policy and may provide an orderingservice for all blockchain nodes. A blockchain node may initiate ablockchain action (such as an authentication) and seek to write to ablockchain immutable ledger stored in the blockchain, a copy of whichmay also be stored on the underpinning physical infrastructure.

The blockchain transactions 620 are stored in memory of computers as thetransactions are received and approved by the consensus model dictatedby the members' nodes. Approved transactions 626 are stored in currentblocks of the blockchain and committed to the blockchain via a committalprocedure which includes performing a hash of the data contents of thetransactions in a current block and referencing a previous hash of aprevious block. Within the blockchain, one or more smart contracts 630may exist that define the terms of transaction agreements and actionsincluded in smart contract executable application code 632, such asregistered recipients, vehicle features, requirements, permissions,sensor thresholds, etc. The code may be configured to identify whetherrequesting entities are registered to receive vehicle services, whatservice features they are entitled/required to receive given theirprofile statuses and whether to monitor their actions in subsequentevents. For example, when a service event occurs and a user is riding inthe vehicle, the sensor data monitoring may be triggered and a certainparameter, such as a vehicle charge level, may be identified as beingabove/below a particular threshold for a particular period of time, thenthe result may be a change to a current status which requires an alertto be sent to the managing party (i.e., vehicle owner, vehicle operator,server, etc.) so the service can be identified and stored for reference.The vehicle sensor data collected may be based on types of sensor dataused to collect information about vehicle's status. The sensor data mayalso be the basis for the vehicle event data 634, such as a location(s)to be traveled, an average speed, a top speed, acceleration rates,whether there were any collisions, was the expected route taken, what isthe next destination, whether safety measures are in place, whether thevehicle has enough charge/fuel, etc. All such information may be thebasis of smart contract terms 630, which are then stored in ablockchain. For example, sensor thresholds stored in the smart contractcan be used as the basis for whether a detected service is necessary andwhen and where the service should be performed.

FIG. 6B illustrates a shared ledger configuration, according to exampleembodiments. Referring to FIG. 6B, the blockchain logic example 640includes a blockchain application interface 642 as an API or plug-inapplication that links to the computing device and execution platformfor a particular transaction. The blockchain configuration 640 mayinclude one or more applications which are linked to applicationprogramming interfaces (APIs) to access and execute storedprogram/application code (e.g., smart contract executable code, smartcontracts, etc.) which can be created according to a customizedconfiguration sought by participants and can maintain their own state,control their own assets, and receive external information. This can bedeployed as an entry and installed, via appending to the distributedledger, on all blockchain nodes.

The smart contract application code 644 provides a basis for theblockchain transactions by establishing application code which whenexecuted causes the transaction terms and conditions to become active.The smart contract 630, when executed, causes certain approvedtransactions 626 to be generated, which are then forwarded to theblockchain platform 652. The platform includes a security/authorization658, computing devices which execute the transaction management 656 anda storage portion 654 as a memory that stores transactions and smartcontracts in the blockchain.

The blockchain platform may include various layers of blockchain data,services (e.g., cryptographic trust services, virtual executionenvironment, etc.), and underpinning physical computer infrastructurethat may be used to receive and store new entries and provide access toauditors which are seeking to access data entries. The blockchain mayexpose an interface that provides access to the virtual executionenvironment necessary to process the program code and engage thephysical infrastructure. Cryptographic trust services may be used toverify entries such as asset exchange entries and keep informationprivate.

The blockchain architecture configuration of FIGS. 6A and 6B may processand execute program/application code via one or more interfaces exposed,and services provided, by the blockchain platform. As a non-limitingexample, smart contracts may be created to execute reminders, updates,and/or other notifications subject to the changes, updates, etc. Thesmart contracts can themselves be used to identify rules associated withauthorization and access requirements and usage of the ledger. Forexample, the information may include a new entry, which may be processedby one or more processing entities (e.g., processors, virtual machines,etc.) included in the blockchain layer. The result may include adecision to reject or approve the new entry based on the criteriadefined in the smart contract and/or a consensus of the peers. Thephysical infrastructure may be utilized to retrieve any of the data orinformation described herein.

Within smart contract executable code, a smart contract may be createdvia a high-level application and programming language, and then writtento a block in the blockchain. The smart contract may include executablecode which is registered, stored, and/or replicated with a blockchain(e.g., distributed network of blockchain peers). An entry is anexecution of the smart contract code, which can be performed in responseto conditions associated with the smart contract being satisfied. Theexecuting of the smart contract may trigger a trusted modification(s) toa state of a digital blockchain ledger. The modification(s) to theblockchain ledger caused by the smart contract execution may beautomatically replicated throughout the distributed network ofblockchain peers through one or more consensus protocols.

The smart contract may write data to the blockchain in the format ofkey-value pairs. Furthermore, the smart contract code can read thevalues stored in a blockchain and use them in application operations.The smart contract code can write the output of various logic operationsinto the blockchain. The code may be used to create a temporary datastructure in a virtual machine or other computing platform. Data writtento the blockchain can be public and/or can be encrypted and maintainedas private. The temporary data that is used/generated by the smartcontract is held in memory by the supplied execution environment, thendeleted once the data needed for the blockchain is identified.

A smart contract executable code may include the code interpretation ofa smart contract, with additional features. As described herein, thesmart contract executable code may be program code deployed on acomputing network, where it is executed and validated by chainvalidators together during a consensus process. The smart contractexecutable code receives a hash and retrieves from the blockchain a hashassociated with the data template created by use of a previously storedfeature extractor. If the hashes of the hash identifier and the hashcreated from the stored identifier template data match, then the smartcontract executable code sends an authorization key to the requestedservice. The smart contract executable code may write to the blockchaindata associated with the cryptographic details.

FIG. 6C illustrates a blockchain configuration for storing blockchaintransaction data, according to example embodiments. Referring to FIG.6C, the example configuration 660 provides for the vehicle 662, the userdevice 664 and a server 666 sharing information with a distributedledger (i.e., blockchain) 668. The server may represent a serviceprovider entity inquiring with a vehicle service provider to share userprofile rating information in the event that a known and establisheduser profile is attempting to rent a vehicle with an established ratedprofile. The server 666 may be receiving and processing data related toa vehicle's service requirements. As the service events occur, such asthe vehicle sensor data indicates a need for fuel/charge, a maintenanceservice, etc., a smart contract may be used to invoke rules, thresholds,sensor information gathering, etc., which may be used to invoke thevehicle service event. The blockchain transaction data 670 is saved foreach transaction, such as the access event, the subsequent updates to avehicle's service status, event updates, etc. The transactions mayinclude the parties, the requirements (e.g., 18 years of age, serviceeligible candidate, valid driver's license, etc.), compensation levels,the distance traveled during the event, the registered recipientspermitted to access the event and host a vehicle service,rights/permissions, sensor data retrieved during the vehicle eventoperation to log details of the next service event and identify avehicle's condition status, and thresholds used to make determinationsabout whether the service event was completed and whether the vehicle'scondition status has changed.

FIG. 6D illustrates blockchain blocks 680 that can be added to adistributed ledger, according to example embodiments, and contents ofblock structures 682A to 682 n. Referring to FIG. 6D, clients (notshown) may submit entries to blockchain nodes to enact activity on theblockchain. As an example, clients may be applications that act onbehalf of a requester, such as a device, person or entity to proposeentries for the blockchain. The plurality of blockchain peers (e.g.,blockchain nodes) may maintain a state of the blockchain network and acopy of the distributed ledger. Different types of blockchainnodes/peers may be present in the blockchain network including endorsingpeers, which simulate and endorse entries proposed by clients andcommitting peers which verify endorsements, validate entries, and commitentries to the distributed ledger. In this example, the blockchain nodesmay perform the role of endorser node, committer node, or both.

The instant system includes a blockchain which stores immutable,sequenced records in blocks, and a state database (current world state)maintaining a current state of the blockchain. One distributed ledgermay exist per channel and each peer maintains its own copy of thedistributed ledger for each channel of which they are a member. Theinstant blockchain is an entry log, structured as hash-linked blockswhere each block contains a sequence of N entries. Blocks may includevarious components such as those shown in FIG. 6D. The linking of theblocks may be generated by adding a hash of a prior block's headerwithin a block header of a current block. In this way, all entries onthe blockchain are sequenced and cryptographically linked togetherpreventing tampering with blockchain data without breaking the hashlinks. Furthermore, because of the links, the latest block in theblockchain represents every entry that has come before it. The instantblockchain may be stored on a peer file system (local or attachedstorage), which supports an append-only blockchain workload.

The current state of the blockchain and the distributed ledger may bestored in the state database. Here, the current state data representsthe latest values for all keys ever included in the chain entry log ofthe blockchain. Smart contract executable code invocations executeentries against the current state in the state database. To make thesesmart contract executable code interactions extremely efficient, thelatest values of all keys are stored in the state database. The statedatabase may include an indexed view into the entry log of theblockchain, it can therefore be regenerated from the chain at any time.The state database may automatically get recovered (or generated ifneeded) upon peer startup, before entries are accepted.

Endorsing nodes receive entries from clients and endorse the entry basedon simulated results. Endorsing nodes hold smart contracts, whichsimulate the entry proposals. When an endorsing node endorses an entry,the endorsing nodes create an entry endorsement which is a signedresponse from the endorsing node to the client application indicatingthe endorsement of the simulated entry. The method of endorsing an entrydepends on an endorsement policy, which may be specified within smartcontract executable code. An example of an endorsement policy is “themajority of endorsing peers must endorse the entry.” Different channelsmay have different endorsement policies. Endorsed entries are forward bythe client application to an ordering service.

The ordering service accepts endorsed entries, orders them into a block,and delivers the blocks to the committing peers. For example, theordering service may initiate a new block when a threshold of entrieshas been reached, a timer times out, or another condition. In thisexample, blockchain node is a committing peer that has received a datablock 682A for storage on the blockchain. The ordering service may bemade up of a cluster of orderers. The ordering service does not processentries, smart contracts, or maintain the shared ledger. Rather, theordering service may accept the endorsed entries and specifies the orderin which those entries are committed to the distributed ledger. Thearchitecture of the blockchain network may be designed such that thespecific implementation of ‘ordering’ (e.g., Solo, Kafka, BFT, etc.)becomes a pluggable component.

Entries are written to the distributed ledger in a consistent order. Theorder of entries is established to ensure that the updates to the statedatabase are valid when they are committed to the network. Unlike acryptocurrency blockchain system (e.g., Bitcoin, etc.) where orderingoccurs through the solving of a cryptographic puzzle, or mining, in thisexample the parties of the distributed ledger may choose the orderingmechanism that best suits that network.

Referring to FIG. 6D, a block 682A (also referred to as a data block)that is stored on the blockchain and/or the distributed ledger mayinclude multiple data segments such as a block header 684A to 684 n,transaction specific data 686A to 686 n, and block metadata 688A to 688n. It should be appreciated that the various depicted blocks and theircontents, such as block 682A and its contents are merely for purposes ofan example and are not meant to limit the scope of the exampleembodiments. In some cases, both the block header 684A and the blockmetadata 688A may be smaller than the transaction specific data 686Awhich stores entry data; however, this is not a requirement. The block682A may store transactional information of N entries (e.g., 100, 500,1000, 2000, 3000, etc.) within the block data 690A to 690 n. The block682A may also include a link to a previous block (e.g., on theblockchain) within the block header 684A. In particular, the blockheader 684A may include a hash of a previous block's header. The blockheader 684A may also include a unique block number, a hash of the blockdata 690A of the current block 682A, and the like. The block number ofthe block 682A may be unique and assigned in an incremental/sequentialorder starting from zero. The first block in the blockchain may bereferred to as a genesis block, which includes information about theblockchain, its members, the data stored therein, etc.

The block data 690A may store entry information of each entry that isrecorded within the block. For example, the entry data may include oneor more of a type of the entry, a version, a timestamp, a channel ID ofthe distributed ledger, an entry ID, an epoch, a payload visibility, asmart contract executable code path (deploy tx), a smart contractexecutable code name, a smart contract executable code version, input(smart contract executable code and functions), a client (creator)identify such as a public key and certificate, a signature of theclient, identities of endorsers, endorser signatures, a proposal hash,smart contract executable code events, response status, namespace, aread set (list of key and version read by the entry, etc.), a write set(list of key and value, etc.), a start key, an end key, a list of keys,a Merkel tree query summary, and the like. The entry data may be storedfor each of the N entries.

In some embodiments, the block data 690A may also store transactionspecific data 686A which adds additional information to the hash-linkedchain of blocks in the blockchain. Accordingly, the data 686A can bestored in an immutable log of blocks on the distributed ledger. Some ofthe benefits of storing such data 686A are reflected in the variousembodiments disclosed and depicted herein. The block metadata 688A maystore multiple fields of metadata (e.g., as a byte array, etc.).Metadata fields may include signature on block creation, a reference toa last configuration block, an entry filter identifying valid andinvalid entries within the block, last offset persisted of an orderingservice that ordered the block, and the like. The signature, the lastconfiguration block, and the orderer metadata may be added by theordering service. Meanwhile, a committer of the block (such as ablockchain node) may add validity/invalidity information based on anendorsement policy, verification of read/write sets, and the like. Theentry filter may include a byte array of a size equal to the number ofentries in the block data 690A and a validation code identifying whetheran entry was valid/invalid.

The other blocks 682B to 682 n in the blockchain also have headers,files, and values. However, unlike the first block 682A, each of theheaders 684A to 684 n in the other blocks includes the hash value of animmediately preceding block. The hash value of the immediately precedingblock may be just the hash of the header of the previous block or may bethe hash value of the entire previous block. By including the hash valueof a preceding block in each of the remaining blocks, a trace can beperformed from the Nth block back to the genesis block (and theassociated original file) on a block-by-block basis, as indicated byarrows 692, to establish an auditable and immutable chain-of-custody.

The above embodiments may be implemented in hardware, in a computerprogram executed by a processor, in firmware, or in a combination of theabove. A computer program may be embodied on a computer readable medium,such as a storage medium. For example, a computer program may reside inrandom access memory (“RAM”), flash memory, read-only memory (“ROM”),erasable programmable read-only memory (“EPROM”), electrically erasableprogrammable read-only memory (“EEPROM”), registers, hard disk, aremovable disk, a compact disk read-only memory (“CD-ROM”), or any otherform of storage medium known in the art.

An exemplary storage medium may be coupled to the processor such thatthe processor may read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor. The processor and the storage medium may reside in anapplication specific integrated circuit (“ASIC”). In the alternative,the processor and the storage medium may reside as discrete components.For example, FIG. 7 illustrates an example computer system architecture700, which may represent or be integrated in any of the above-describedcomponents, etc.

FIG. 7 is not intended to suggest any limitation as to the scope of useor functionality of embodiments of the application described herein.Regardless, the computing node 700 is capable of being implementedand/or performing any of the functionality set forth hereinabove.

In computing node 700 there is a computer system/server 702, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 702 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 702 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 702 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 7 , computer system/server 702 in cloud computing node700 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 702 may include, but are notlimited to, one or more processors or processing units 704, a systemmemory 706, and a bus that couples various system components includingsystem memory 706 to processor 704.

The bus represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system/server 702 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 702, and it includes both volatileand non-volatile media, removable and non-removable media. System memory706, in one embodiment, implements the flow diagrams of the otherfigures. The system memory 706 can include computer system readablemedia in the form of volatile memory, such as random-access memory (RAM)708 and/or cache memory 710. Computer system/server 702 may furtherinclude other removable/non-removable, volatile/non-volatile computersystem storage media. By way of example only, memory 706 can be providedfor reading from and writing to a non-removable, non-volatile magneticmedia (not shown and typically called a “hard drive”). Although notshown, a magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk (e.g., a “floppy disk”), and anoptical disk drive for reading from or writing to a removable,non-volatile optical disk such as a CD-ROM, DVD-ROM or other opticalmedia can be provided. In such instances, each can be connected to thebus by one or more data media interfaces. As will be further depictedand described below, memory 706 may include at least one program producthaving a set (e.g., at least one) of program modules that are configuredto carry out the functions of various embodiments of the application.

Program/utility, having a set (at least one) of program modules, may bestored in memory 706 by way of example, and not limitation, as well asan operating system, one or more application programs, other programmodules, and program data. Each of the operating system, one or moreapplication programs, other program modules, and program data or somecombination thereof, may include an implementation of a networkingenvironment. Program modules generally carry out the functions and/ormethodologies of various embodiments of the application as describedherein.

As will be appreciated by one skilled in the art, aspects of the presentapplication may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present application may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present application may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Computer system/server 702 may also communicate with one or moreexternal devices via an I/O device 712 (such as an I/O adapter), whichmay include a keyboard, a pointing device, a display, a voicerecognition module, etc., one or more devices that enable a user tointeract with computer system/server 702, and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 702 tocommunicate with one or more other computing devices. Such communicationcan occur via I/O interfaces of the device 712. Still yet, computersystem/server 702 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via a network adapter. As depicted,device 712 communicates with the other components of computersystem/server 702 via a bus. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system/server 702. Examples, include, but arenot limited to: microcode, device drivers, redundant processing units,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

Although an exemplary embodiment of at least one of a system, method,and non-transitory computer readable medium has been illustrated in theaccompanied drawings and described in the foregoing detaileddescription, it will be understood that the application is not limitedto the embodiments disclosed, but is capable of numerous rearrangements,modifications, and substitutions as set forth and defined by thefollowing claims. For example, the capabilities of the system of thevarious figures can be performed by one or more of the modules orcomponents described herein or in a distributed architecture and mayinclude a transmitter, receiver or pair of both. For example, all orpart of the functionality performed by the individual modules, may beperformed by one or more of these modules. Further, the functionalitydescribed herein may be performed at various times and in relation tovarious events, internal or external to the modules or components. Also,the information sent between various modules can be sent between themodules via at least one of: a data network, the Internet, a voicenetwork, an Internet Protocol network, a wireless device, a wired deviceand/or via plurality of protocols. Also, the messages sent or receivedby any of the modules may be sent or received directly and/or via one ormore of the other modules.

One skilled in the art will appreciate that a “system” could be embodiedas a personal computer, a server, a console, a personal digitalassistant (PDA), a cell phone, a tablet computing device, a smartphoneor any other suitable computing device, or combination of devices.Presenting the above-described functions as being performed by a“system” is not intended to limit the scope of the present applicationin any way but is intended to provide one example of many embodiments.Indeed, methods, systems and apparatuses disclosed herein may beimplemented in localized and distributed forms consistent with computingtechnology.

It should be noted that some of the system features described in thisspecification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom verylarge-scale integration (VLSI) circuits or gate arrays, off-the-shelfsemiconductors such as logic chips, transistors, or other discretecomponents. A module may also be implemented in programmable hardwaredevices such as field programmable gate arrays, programmable arraylogic, programmable logic devices, graphics processing units, or thelike.

A module may also be at least partially implemented in software forexecution by various types of processors. An identified unit ofexecutable code may, for instance, comprise one or more physical orlogical blocks of computer instructions that may, for instance, beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether but may comprise disparate instructions stored in differentlocations which, when joined logically together, comprise the module andachieve the stated purpose for the module. Further, modules may bestored on a computer-readable medium, which may be, for instance, a harddisk drive, flash device, random access memory (RAM), tape, or any othersuch medium used to store data.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

It will be readily understood that the components of the application, asgenerally described and illustrated in the figures herein, may bearranged and designed in a wide variety of different configurations.Thus, the detailed description of the embodiments is not intended tolimit the scope of the application as claimed but is merelyrepresentative of selected embodiments of the application.

One having ordinary skill in the art will readily understand that theabove may be practiced with steps in a different order, and/or withhardware elements in configurations that are different than those whichare disclosed. Therefore, although the application has been describedbased upon these preferred embodiments, it would be apparent to those ofskill in the art that certain modifications, variations, and alternativeconstructions would be apparent.

While preferred embodiments of the present application have beendescribed, it is to be understood that the embodiments described areillustrative only and the scope of the application is to be definedsolely by the appended claims when considered with a full range ofequivalents and modifications (e.g., protocols, hardware devices,software platforms etc.) thereto.

What is claimed is:
 1. A method, comprising: sending, by ahardware-implemented server, an instruction to a transport toautonomously take an action based on a severity of an event that isdetermined by data from a primary device located in an interior of thetransport and a consensus of data from a plurality of secondary devicesremote from the transport and within a range of the transport.
 2. Themethod of claim 1, wherein the data from the primary device and the datafrom a plurality of secondary devices comprises: sensor data relating toanomalous behavior of the transport from the plurality of secondarydevices.
 3. The method of claim 1, further comprising: identifying theconsensus of data when the data provided by a majority of the pluralityof secondary devices is within a common range corresponding to theseverity of the event.
 4. The method of claim 1, further comprising:sending, by the hardware-implemented server, a first notification to theprimary device and a second notification containing content that isdifferent than a content of the first notification to the plurality ofsecondary devices.
 5. The method of claim 1, further comprising:identifying, by the hardware-implemented server, a state of thetransport based on data collected by each secondary device, of theplurality of secondary devices, at a particular time, wherein the stateis related to the event, and wherein a degree of the data from theplurality of secondary devices relates to the severity of the event. 6.The method of claim 1, further comprising: identifying an action to takebased on the severity of the event, the action comprising at least oneof: notifying the transport in response to the severity of the event islow; notifying the plurality of devices in response to the severity ofthe event is moderate; and contacting one or more first responders inresponse to the severity of the event is high.
 7. The method of claim 1,comprising: creating, by the hardware-implemented server, a blockchaintransaction based on a smart contract and one or more of: the data fromthe primary device; the data from the plurality of secondary devices; alocation of the plurality of secondary devices; a type of the pluralityof secondary devices; times of receipt of the data from the plurality ofsecondary devices; a type of the severity of the event; and a type ofthe action to take; and storing, by the hardware-implemented server, theblockchain transaction in a blockchain ledger based on a smart contract.8. A hardware-implemented server, comprising: a memory storing one ormore instructions; and a processor that when executing the one or moreinstructions is configured to: send an instruction to a transport toautonomously take an action based on a severity of an event that isdetermined by data from a primary device located in an interior of thetransport and a consensus of data from a plurality of secondary devicesremote from the transport and within a range of the transport.
 9. Thehardware-implemented server of claim 8, wherein the data from theprimary device and the data from a plurality of secondary devicescomprises: sensor data related to anomalous behavior of the transportfrom the plurality of secondary devices.
 10. The hardware-implementedserver of claim 8, wherein, when the processor is configured to identifya severity of the event, the processor is further configured to:identify the consensus when the secondary data provided by a majority ofthe plurality of secondary devices is within a common range thatcorresponds to the severity of the event.
 11. The hardware-implementedserver of claim 8, wherein, when the processor is configured to identifya severity of the event, the processor is further configured to: send afirst notification to the primary device and a second notification thatcontains content that is different than a content of the firstnotification to the plurality of secondary devices.
 12. Thehardware-implemented server of claim 8, wherein the processor is furtherconfigured to: identify a state of the transport based on data collectedby each secondary device, of the plurality of secondary devices, at aparticular time, wherein the state is related to the event, and whereina degree of the data from the plurality of secondary devices relates tothe severity of the event.
 13. The hardware-implemented server of claim8, wherein the processor is further configured to: identify an action totake based on the severity, wherein the action comprises at least oneof: notify the transport in response to the severity of the event islow; notify the plurality of devices in response to the severity of theevent is moderate; and contact one or more first responders in responseto the severity of the event is high.
 14. The hardware-implementedserver of claim 8, wherein the processor is configured to: create ablockchain transaction based on a smart contract and one or more of: thedata from the primary device; the data from the plurality of secondarydevices; a location of the plurality of secondary devices; a type of theplurality of secondary devices; times of receipt of the data from theplurality of secondary devices; a type of the severity of the event; anda type of the action to take; and store the blockchain transaction in ablockchain ledger based on a smart contract.
 15. A non-transitorycomputer readable medium comprising one or more instructions that whenexecuted by a processor of a server cause the processor to perform:sending an instruction to a transport to autonomously take an actionbased on a severity of an event that is determined by data from aprimary device located in an interior of the transport and a consensusof data from a plurality of secondary devices remote from the transportand within a range of the transport.
 16. The non-transitory computerreadable medium of claim 15, wherein the primary data and the secondarydata comprises: wherein the data from the primary device and the datafrom a plurality of secondary devices comprises: sensor data relating toanomalous behavior of the transport from the plurality of secondarydevices.
 17. The non-transitory computer readable medium of claim 15,wherein the one or more instructions further causes the processor toperform: sending a first notification to the primary device and a secondnotification containing content that is different than a content of thefirst notification to the plurality of secondary devices.
 18. Thenon-transitory computer readable medium of claim 15, wherein the one ormore instructions further causes the processor to perform: identifying,by the hardware-implemented server, a state of the transport based ondata collected by each secondary device, of the plurality of secondarydevices, at a particular time, wherein the state is related to theevent, and wherein a degree of the data from the plurality of secondarydevices relates to the severity of the event.
 19. The non-transitorycomputer readable medium of claim 15, wherein the one or moreinstructions further causes the processor to perform: identifying anaction to take based on the severity of the event, the action comprisingat least one of: notifying the transport in response to the severity ofthe event is low; notifying the plurality of devices in response to theseverity of the event is moderate; and contacting one or more firstresponders in response to the severity of the event is high.
 20. Thenon-transitory computer readable medium of claim 15, wherein the one ormore instructions further cause the processor to perform: creating, bythe hardware-implemented server, a blockchain transaction based on asmart contract and one or more of: the data from the primary device; thedata from the plurality of secondary devices; a location of theplurality of secondary devices; a type of the plurality of secondarydevices; times of receipt of the data from the plurality of secondarydevices; a type of the severity of the event; and a type of the actionto take; and storing, by the hardware-implemented server, the blockchaintransaction in a blockchain ledger based on a smart contract.